def test_scikit_metrics():
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X,
y,
train_size=0.6,
random_state=2)
clf = DecisionTreeClassifier(max_depth=1, random_state=1)
training_errors, test_errors = plot_learning_curves(X_train,
y_train,
X_test,
y_test,
clf,
kind='training_size',
suppress_plot=True,
scoring='accuracy')
desired1 = [0.68, 0.67, 0.68, 0.67, 0.7, 0.69, 0.69, 0.78, 0.78, 0.78]
desired2 = [0.65, 0.65, 0.65, 0.65, 0.57, 0.55, 0.65, 0.65, 0.55, 0.55]
np.testing.assert_almost_equal(training_errors, desired1, decimal=2)
np.testing.assert_almost_equal(test_errors, desired2, decimal=2)
def test_training_size():
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.6, random_state=2)
clf = DecisionTreeClassifier(max_depth=1, random_state=1)
training_errors, test_errors = plot_learning_curves(X_train, y_train, X_test, y_test, clf, kind='training_size', suppress_plot=True)
desired1 = [0.32, 0.33, 0.32, 0.33, 0.30, 0.31, 0.31, 0.22, 0.22, 0.22]
desired2 = [0.35, 0.35, 0.35, 0.35, 0.43, 0.45, 0.35, 0.35, 0.45, 0.45]
np.testing.assert_almost_equal(training_errors, desired1, decimal=2)
np.testing.assert_almost_equal(test_errors, desired2, decimal=2)
def test_scikit_metrics():
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.6, random_state=2)
clf = DecisionTreeClassifier(max_depth=1, random_state=1)
training_errors, test_errors = plot_learning_curves(X_train, y_train, X_test, y_test, clf, kind='training_size', suppress_plot=True, scoring='accuracy')
desired1 = [0.68, 0.67, 0.68, 0.67, 0.7, 0.69, 0.69, 0.78, 0.78, 0.78]
desired2 = [0.65, 0.65, 0.65, 0.65, 0.57, 0.55, 0.65, 0.65, 0.55, 0.55]
np.testing.assert_almost_equal(training_errors, desired1, decimal=2)
np.testing.assert_almost_equal(test_errors, desired2, decimal=2)
def test_training_size():
iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = (train_test_split(X, y,
train_size=0.6, random_state=2))
clf = DecisionTreeClassifier(max_depth=1, random_state=1)
training_errors, test_errors = (plot_learning_curves(X_train, y_train,
X_test, y_test, clf, suppress_plot=True))
desired1 = [0.32, 0.33, 0.32, 0.33, 0.30, 0.31, 0.31, 0.22, 0.22, 0.22]
desired2 = [0.35, 0.35, 0.35, 0.35, 0.43, 0.45, 0.35, 0.35, 0.45, 0.45]
np.testing.assert_almost_equal(training_errors, desired1, decimal=2)
np.testing.assert_almost_equal(test_errors, desired2, decimal=2)
''' Author : Oguzhan Gencoglu Contact : [email protected] Created : 30.01.2016 Latest Version : 30.01.2016 ''' from mlxtend.evaluate import plot_learning_curves from sklearn.cross_validation import train_test_split from sklearn.tree import DecisionTreeClassifier X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.6, random_seed=2) clf = DecisionTreeClassifier(max_depth=1) plot_learning_curves(X_train, y_train, X_test, y_test, clf, kind='training_size') plt.show() plot_learning_curves(X_train, y_train, X_test, y_test, clf, kind='n_features') plt.show()
# TODO error from mlxtend.evaluate import plot_learning_curves import matplotlib.pyplot as plt from mlxtend.data import iris_data from mlxtend.preprocessing import shuffle_arrays_unison from sklearn.neighbors import KNeighborsClassifier # Loading some example data X, y = iris_data() X, y = shuffle_arrays_unison(arrays=[X, y]) X_train, X_test = X[:100], X[100:] y_train, y_test = y[:100], y[100:] clf = KNeighborsClassifier(n_neighbors=5) plot_learning_curves(X_train, y_train, X_test, y_test, clf) plt.show()
